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ab4b4f22fd
solver to minimize the cycle cost to visit all changes in our estimated list. This is fortunately a tractable (though slow) computation that does give improvements on the previous heuristic at the level of ~6% better throughput. This opcode schedule prefers to group by page and vary over content, so implement a fast heuristic that does that. This scheduler is within 2% of the TSP solution.
180 lines
6.7 KiB
Python
180 lines
6.7 KiB
Python
import skvideo.io
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import skvideo.datasets
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from PIL import Image
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import numpy as np
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import screen
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CYCLES = 1024 * 1024
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MAX_OUT = 20 * 1024
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VIDEO_FPS = 30
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APPLE_FPS = 10
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# Old naive XOR algorithm:
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#
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# stores=1894, content changes=15, page changes=365
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# Frame 0, 2654 bytes, similarity = 0.850856
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# stores=1750, content changes=19, page changes=444
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# Frame 3, 2676 bytes, similarity = 0.903088
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# stores=1648, content changes=20, page changes=501
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# Frame 6, 2690 bytes, similarity = 0.922024
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# stores=1677, content changes=18, page changes=486
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# Frame 9, 2685 bytes, similarity = 0.912723
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# stores=1659, content changes=18, page changes=497
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# Frame 12, 2689 bytes, similarity = 0.923438
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# stores=1681, content changes=17, page changes=485
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# Frame 15, 2685 bytes, similarity = 0.922656
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# stores=1686, content changes=17, page changes=482
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# Frame 18, 2684 bytes, similarity = 0.921912
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# stores=1669, content changes=17, page changes=492
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# New
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# stores=2260, content changes=277, page changes=125
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# Frame 0, 3064 bytes, similarity = 0.874740
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# stores=2162, content changes=325, page changes=131
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# Frame 3, 3074 bytes, similarity = 0.925670
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# stores=2241, content changes=313, page changes=102
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# Frame 6, 3071 bytes, similarity = 0.936942
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# stores=2265, content changes=313, page changes=90
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# Frame 9, 3071 bytes, similarity = 0.931882
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# stores=2225, content changes=334, page changes=91
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# Frame 12, 3075 bytes, similarity = 0.929427
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# stores=2216, content changes=342, page changes=89
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# Frame 15, 3078 bytes, similarity = 0.919978
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# stores=2222, content changes=339, page changes=88
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# Optimized new
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# Fullness = 1.384560, cycles = 90738/104857 budget
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# stores=1872, content changes=15, page changes=352
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# Frame 0, 2606 bytes, similarity = 0.849219
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# Fullness = 1.452588, cycles = 110009/104857 budget
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# stores=2163, content changes=28, page changes=472
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# Frame 3, 3163 bytes, similarity = 0.924256
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# Fullness = 1.577072, cycles = 113843/104857 budget
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# stores=2062, content changes=30, page changes=577
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# Frame 6, 3276 bytes, similarity = 0.939918
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# Fullness = 1.597466, cycles = 106213/104857 budget
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# stores=1899, content changes=29, page changes=550
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# Frame 9, 3057 bytes, similarity = 0.928274
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# Fullness = 1.615001, cycles = 106008/104857 budget
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# stores=1875, content changes=27, page changes=561
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# Frame 12, 3051 bytes, similarity = 0.933854
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# Fullness = 1.639691, cycles = 106460/104857 budget
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# stores=1855, content changes=30, page changes=575
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# Frame 15, 3065 bytes, similarity = 0.929725
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# Fullness = 1.635406, cycles = 104583/104857 budget
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# stores=1827, content changes=30, page changes=562
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# TSP solver
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# Fullness = 1.336189, cycles = 87568/104857 budget
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# stores=1872, content changes=320, page changes=32
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# Frame 0, 2576 bytes, similarity = 0.849219
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# Fullness = 1.386065, cycles = 108771/104857 budget
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# stores=2242, content changes=452, page changes=33
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# Frame 3, 3212 bytes, similarity = 0.927604
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# Fullness = 1.482284, cycles = 112136/104857 budget
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# stores=2161, content changes=552, page changes=33
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# Frame 6, 3331 bytes, similarity = 0.943415
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# Fullness = 1.501014, cycles = 106182/104857 budget
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# stores=2021, content changes=535, page changes=33
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# Frame 9, 3157 bytes, similarity = 0.934263
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# Fullness = 1.523818, cycles = 106450/104857 budget
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# stores=1995, content changes=554, page changes=33
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# Frame 12, 3169 bytes, similarity = 0.939844
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# Fullness = 1.543029, cycles = 106179/104857 budget
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# stores=1966, content changes=566, page changes=33
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# Frame 15, 3164 bytes, similarity = 0.935231
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# Fullness = 1.538659, cycles = 104560/104857 budget
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# stores=1941, content changes=554, page changes=33
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# page first
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# Fullness = 1.366463, cycles = 89552/104857 budget
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# stores=1872, content changes=352, page changes=32
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# Frame 0, 2640 bytes, similarity = 0.849219
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# Fullness = 1.413155, cycles = 108440/104857 budget
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# stores=2192, content changes=476, page changes=32
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# Frame 3, 3208 bytes, similarity = 0.925744
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# Fullness = 1.516888, cycles = 112554/104857 budget
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# stores=2120, content changes=583, page changes=32
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# Frame 6, 3350 bytes, similarity = 0.942187
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# Fullness = 1.535086, cycles = 106115/104857 budget
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# stores=1975, content changes=561, page changes=32
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# Frame 9, 3161 bytes, similarity = 0.932106
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# Fullness = 1.553913, cycles = 106143/104857 budget
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# stores=1951, content changes=575, page changes=32
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# Frame 12, 3165 bytes, similarity = 0.937835
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# Fullness = 1.571548, cycles = 106047/104857 budget
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# stores=1927, content changes=587, page changes=32
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# Frame 15, 3165 bytes, similarity = 0.933259
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# Fullness = 1.572792, cycles = 104940/104857 budget
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# stores=1906, content changes=581, page changes=32
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def main():
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s = screen.Screen()
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decoder = screen.Screen()
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videogen = skvideo.io.vreader("CoffeeCup-H264-75.mov")
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with open("out.bin", "wb") as out:
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bytes_out = 0
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# Estimated opcode overhead, i.e. ratio of extra cycles from opcodes
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fullness = 1.6
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for idx, frame in enumerate(videogen):
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if idx % (VIDEO_FPS // APPLE_FPS):
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continue
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im = Image.fromarray(frame)
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im = im.resize((screen.Frame.XMAX, screen.Frame.YMAX))
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im = im.convert("1")
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im = np.array(im)
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# im.show()
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f = screen.Frame(im)
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cycle_budget = int(CYCLES / APPLE_FPS)
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stream = bytes(s.update(f, cycle_budget, fullness))
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fullness *= s.cycles / cycle_budget
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print("Fullness = %f, cycles = %d/%d budget" % (
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fullness, s.cycles, cycle_budget))
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# Assert that the opcode stream reconstructs the same screen
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(num_content_stores, num_content_changes,
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num_page_changes) = decoder.from_stream(iter(stream))
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assert np.array_equal(decoder.screen, s.screen)
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print("stores=%d, content changes=%d, page changes=%d" % (
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num_content_stores, num_content_changes,
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num_page_changes))
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# print(" ".join("%02x(%02d)" % (b, b) for b in stream))
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# assert that the screen decodes to the original bitmap
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bm = s.to_bitmap()
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# print(np.array(im)[0:5,0:5])
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# print(bm[0:5,0:5])
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# print("Comparing bitmaps")
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# print(np.array(im))
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# print(bm)
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# print(s.screen)
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# assert np.array_equal(bm, im), np.ma.masked_array(
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# bm, np.logical_not(np.logical_xor(bm, im)))
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# d = Image.fromarray(s.screen)
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# d.show()
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bytes_out += len(stream)
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if bytes_out > MAX_OUT:
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break
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print("Frame %d, %d bytes, similarity = %f" % (
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idx, len(stream), s.similarity(im, bm)))
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out.write(stream)
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out.write(bytes(s.done()))
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if __name__ == "__main__":
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main()
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